A Starting Point for Navigating the World of Daily Fantasy Basketball

Fantasy sports, particularly the daily variety in which new lineups are selected each day, are a rapidly growing industry. The two largest companies in the daily fantasy business, DraftKings and Fanduel, have been valued as high as $2 billion. This research focuses on the development of a complete system for daily fantasy basketball, including both the prediction of player performance and the construction of a team. First, a Bayesian random effects model is used to predict an aggregate measure of daily NBA player performance. The predictions are then used to construct teams under the constraints of the game, typically related to a fictional salary cap and player positions. Permutation based and K-nearest neighbors approaches are compared in terms of the identification of “successful” teams—those who would be competitive more often than not based on historical data. We demonstrate the efficacy of our system by comparing our predictions to those from a well-known analytics website, and by simulating daily competitions over the course of the 2015–2016 season. Our results show an expected profit of approximately $9,000 on an initial $500 investment using the K-nearest neighbors approach, a 36% increase relative to using the permutation-based approach alone. Supplementary materials for this article are available online.     

Estimating mixtures of normal distributions via empirical characteristic function

This paper uses the empirical characteristic function (ECF) procedure to estimate the parameters of mixtures of normal distributions. Since the characteristic function is uniformly bounded, the procedure gives estimates that are numerically stable. It is shown that, using Monte Carlo simulation, the finite sample properties of th ECF estimator are very good, even in the case where the popular maximum likelihood estimator fails to exist. An empirical application is illustrated using the monthl excess return of the Nyse value-weighted index.     

戈德尔的市场自由道德理论评介

戴卫·戈德尔是继罗尔斯之后 ,对契约主义思想影响最大 ,同时也带来了最多争议的思想家之一。他从塑造理想市场的需要出发界说人的道德与理性 ,认为二者同市场的运行原则———有限制最大化是和谐一致的。道德与理性的一致性假设虽然能够有效解决市场分析中的市场失灵问题 ,但是 ,这一假设本身依赖于一个理想市场的优先存在。两个假设的相互依赖最终使得戈德尔的道德学说陷入了解释学的循环     

Detection of multiple undocumented change-points using adaptive Lasso

The problem of detecting multiple undocumented change-points in a historical temperature sequence with simple linear trend is formulated by a linear model. We apply adaptive least absolute shrinkage and selection operator (Lasso) to estimate the number and locations of change-points. Model selection criteria are used to choose the Lasso smoothing parameter. As adaptive Lasso may overestimate the number of change-points, we perform post-selection on change-points detected by adaptive Lasso using multivariate t simultaneous confidence intervals. Our method is demonstrated on the annual temperature data (year: 1902–2000) from Tuscaloosa, Alabama.     

Estimating monotone convex functions via sequential shape modification

We propose a sequential method to estimate monotone convex functions that consists of: (i) monotone regression via solving a constrained least square (LS) problem and (ii) convexification of the monotone regression estimate via solving a uniform approximation problem with associated constraints. We show that this method is faster than the constrained LS method. The ratio of computation time increases as data size increases. Moreover, we show that, under an appropriate smoothness condition, the uniform convergence rate achieved by the proposed method is nearly comparable to the best achievable rate for a non-parametric estimate which ignores the shape constraint. Simulation studies show that our method is comparable to the constrained LS method in estimation error. We illustrate our method by analysing ground water level data of wells in Korea.     

多资源受限条件下工程集成管理优化问题研究

本文建立了多资源受限条件下工程集成管理优化的模型,该模型的求解属于国际上公认的NP-hard难题之一,利用基于优先权的编码技术使得模型的求解成为可能,提出同时具有惯性权重和限定因子参数的改进版本微粒群算法,编制其matlab求解源程序,运用在以管道水平定向钻穿越工程为实例的集成管理优化模型中,微粒群算法程序在求解过程表现出了高效的搜索能力,获得了满意的优化结果。最后,着重讨论了在微粒群算法参数设计中微粒个体意识与集体意识的比较分析和微粒群种群规模与协同搜索能力的关系。     

The Bayesian elastic net regression

A Bayesian elastic net approach is presented for variable selection and coefficient estimation in linear regression models. A simple Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the Bayesian elastic net prior for the regression coefficients. The penalty parameters are chosen through an empirical method that maximizes the data marginal likelihood. Both simulated and real data examples show that the proposed method performs well in comparison to the other approaches.     

Non‐Manipulable House Allocation With Rent Control

In many real‐life house allocation problems, rents are bounded from above by price ceilings imposed by a government or a local administration. This is known as rent control. Because some price equilibria may be disqualified given such restrictions, this paper proposes an alternative equilibrium concept, called rationing price equilibrium, tailored to capture the specific features of housing markets with rent control. An allocation rule that always selects a rationing price equilibrium is defined, and it is demonstrated to be constrained efficient and (group) non‐manipulable for “almost all” preference profiles. In its bounding cases, the rule reduces to a number of well‐known mechanisms from the matching literature. In this sense, the housing market with rent control investigated in this paper integrates several of the predominant matching models into a more general framework.     

基于随机化适应性Lasso的高维变量选择

Lasso等惩罚变量选择方法选入模型的变量数受到样本量限制。文献中已有研究变量系数显著性的方法舍弃了未选入模型的变量含有的信息。本文在变量数大于样本量即p>n的高维情况下,使用随机化bootstrap方法获得变量权重,在计算适应性Lasso时构建选择事件的条件分布并剔除系数不显著的变量,以得到最终估计结果。本文的创新点在于提出的方法突破了适应性Lasso可选变量数的限制,当观测数据含有大量干扰变量时能够有效地识别出真实变量与干扰变量。与现有的惩罚变量选择方法相比,多种情境下的模拟研究展示了所提方法在上述两个问题中的优越性。实证研究中对NCI-60癌症细胞系数据进行了分析,结果较以往文献有明显改善。     

LAD准则下的无线传感器网络节点定位方法

多维标度算法广泛应用于无线传感器网络的节点定位。经典的MDS算法通过构造距离平方矩阵(非相似性矩阵)和进行双质心变换,在相似性空间中根据最小二乘准则进行求解。若测量噪声为高斯白噪声,经过变换后,相似性矩阵中元素的误差不再服从高斯分布,基于LS的估计不再是最优的。针对这一问题,用最小绝对值偏差准则改进MDS算法代价函数,对无线传感器网络节点定位进行研究。仿真结果表明,该方法具有良好的稳健性,比经典MDS算法具有更好的定位性能。